import numpy as np

# 創(chuàng)建一個(gè)一維數(shù)組
arr = np.array([1, 2, 3, 4, 5])
print(arr)  # 輸出: [1 2 3 4 5]

# 創(chuàng)建一個(gè)多維數(shù)組
multi_dim_arr = np.array([[1, 2, 3], [4, 5, 6]])
print(multi_dim_arr)
# 輸出:
# [[1 2 3]
#  [4 5 6]]

# 數(shù)組操作
sum_arr = np.sum(arr)
mean_arr = np.mean(arr)
print(sum_arr, mean_arr)  # 輸出: 15 3.0

import pandas as pd

# 創(chuàng)建一個(gè) DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)
# 輸出:
#       Name  Age
# 0   Alice   25
# 1     Bob   30
# 2  Charlie  35

# 數(shù)據(jù)篩選
filtered_df = df[df['Age'] > 30]
print(filtered_df)
# 輸出:
#       Name  Age
# 2  Charlie  35

import matplotlib.pyplot as plt

# 繪制簡(jiǎn)單的折線圖
x = [1, 2, 3, 4, 5]
y = [2, 3, 5, 7, 11]

plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Line Plot')
plt.show()

from scipy import stats

# 計(jì)算兩個(gè)樣本的 t 檢驗(yàn)
sample1 = [1, 2, 3, 4, 5]
sample2 = [2, 3, 4, 5, 6]
t_stat, p_value = stats.ttest_ind(sample1, sample2)
print(t_stat, p_value)  # 輸出: -2.23606797749979 0.06935067780645372

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# 加載數(shù)據(jù)集
iris = load_iris()
X, y = iris.data, iris.target

# 劃分訓(xùn)練集和測(cè)試集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 訓(xùn)練模型
knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

# 預(yù)測(cè)
predictions = knn.predict(X_test)
print(predictions)
# 輸出: [2 1 0 2 0 2 0 1 1 1 2 1 1 1 2 0 1 1 0 0 2 1 0 0 2 0 2 2 2 0]

import tensorflow as tf

# 創(chuàng)建一個(gè)簡(jiǎn)單的神經(jīng)網(wǎng)絡(luò)
model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, activation='relu', input_shape=(4,)),
    tf.keras.layers.Dense(3, activation='softmax')
])

# 編譯模型
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

# 訓(xùn)練模型
model.fit(X_train, y_train, epochs=10, batch_size=10)

import torch
import torch.nn as nn
import torch.optim as optim

# 定義一個(gè)簡(jiǎn)單的神經(jīng)網(wǎng)絡(luò)
class SimpleNet(nn.Module):
    def __init__(self):
        super(SimpleNet, self).__init__()
        self.fc1 = nn.Linear(4, 10)
        self.fc2 = nn.Linear(10, 3)

    def forward(self, x):
        x = torch.relu(self.fc1(x))
        x = self.fc2(x)
        return x

# 實(shí)例化模型
model = SimpleNet()

# 定義損失函數(shù)和優(yōu)化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

# 訓(xùn)練模型
for epoch in range(100):
    optimizer.zero_grad()
    outputs = model(torch.tensor(X_train, dtype=torch.float32))
    loss = criterion(outputs, torch.tensor(y_train, dtype=torch.long))
    loss.backward()
    optimizer.step()

import seaborn as sns

# 繪制箱形圖
sns.boxplot(x='Name', y='Age', data=df)
plt.show()

from sympy import symbols, diff

# 定義符號(hào)變量
x = symbols('x')

# 定義函數(shù)
f = x**2 + 2*x + 1

# 求導(dǎo)
f_prime = diff(f, x)
print(f_prime)  # 輸出: 2*x + 2

import networkx as nx

# 創(chuàng)建一個(gè)簡(jiǎn)單的圖
G = nx.Graph()
G.add_edge('A', 'B')
G.add_edge('B', 'C')
G.add_edge('C', 'A')

# 繪制圖
nx.draw(G, with_labels=True)
plt.show()

import statsmodels.api as sm

# 加載數(shù)據(jù)
data = sm.datasets.fair.load_pandas().data

# 添加常數(shù)項(xiàng)
data['const'] = 1

# 定義因變量和自變量
y = data['affairs']
X = data[['const', 'rate_marriage', 'age', 'yrs_married', 'children', 'religious', 'educ', 'occupation', 'occupation_husb']]

# 擬合線性回歸模型
model = sm.OLS(y, X).fit()
print(model.summary())

import plotly.express as px

# 繪制散點(diǎn)圖
fig = px.scatter(x=[1, 2, 3, 4, 5], y=[2, 3, 5, 7, 11])
fig.show()

import pandas as pd
import matplotlib.pyplot as plt

# 讀取股票數(shù)據(jù)
df = pd.read_csv('stock_data.csv')

# 轉(zhuǎn)換日期格式
df['Date'] = pd.to_datetime(df['Date'])

# 設(shè)置日期為索引
df.set_index('Date', inplace=True)

# 繪制收盤(pán)價(jià)折線圖
plt.figure(figsize=(10, 5))
plt.plot(df['Close'])
plt.xlabel('Date')
plt.ylabel('Closing Price')
plt.title('Stock Closing Price Over Time')
plt.show()